Volume 30 Issue 1
Jan.  2021
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YAN Ruqiang, SHEN Fei, ZHOU Mengjie, “Induction Motor Fault Diagnosis Based on Transfer Principal Component Analysis,” Chinese Journal of Electronics, vol. 30, no. 1, pp. 18-25, 2021, doi: 10.1049/cje.2020.11.003
Citation: YAN Ruqiang, SHEN Fei, ZHOU Mengjie, “Induction Motor Fault Diagnosis Based on Transfer Principal Component Analysis,” Chinese Journal of Electronics, vol. 30, no. 1, pp. 18-25, 2021, doi: 10.1049/cje.2020.11.003

Induction Motor Fault Diagnosis Based on Transfer Principal Component Analysis

doi: 10.1049/cje.2020.11.003

the National Natural Science Foundation of China 51575102

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  • Author Bio:

    SHEN Fei   received the B.S. and M.S. degrees from Southeast University in 2014 and 2016 respectively. Now he is pursuing the Ph.D. degree in School of Instrument Science and Engineering, Southeast University. His main research interest is machine fault diagnosis. (Email: sfseu0311@163.com)

    ZHOU Mengjie   received the B.S. degree from School of Electrical Engineering and Automation, Anhui University in 2015, and received the M.S. degree from School of Instrument Science and Engineering, Southeast University in 2018. His main research interest is machine learning. (Email: mjz2861@163.com)

  • Corresponding author: YAN Ruqiang  (corresponding author) received the B.S. and M.E. degrees from University of Science and Technology of China in 1997 and 2002 respectively, and received the Ph.D. degree in 2007 from University of Massachusetts, Amherst. Now he is a professor and Ph.D. supervisor in Xi'an Jiaotong University. His main research interests include machine condition monitoring and fault diagnosis, signal processing, and wireless sensor networks. (Email: yanruqiang@xjtu.edu.cn)
  • Received Date: 2019-08-26
  • Accepted Date: 2020-05-12
  • Publish Date: 2021-01-01
  • This paper presents a transfer learning-based approach for induction motor fault diagnosis, where the Transfer principal component analysis (TPCA) is proposed to improve diagnostic performance of the induction motors under various working conditions. TPCA is developed to minimize the distribution difference between training and testing data by mapping cross-domain data into a shared latent space in which domain difference can be reduced. The trained model can achieve a good performance in testing data by using the learned features consisting of common latent principal components. Experimental results show that the proposed approach outperforms traditional machine learning techniques and can diagnose induction motor fault under various working conditions effectively.
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